2 citations · 2 across the 4 of their papers we have counts for
4 papers
How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling
Katrin Rohrbacher, Björn Nieth, Emmanuelle Salin +2
In this paper, we analyze how Large Language Models (LLMs) employ worldbuilding strategies, focusing on setting as one measurable dimension of storyworld construction. We compare 1…
How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework
Björn Nieth, Marianna Gracheva, Michaela Mahlberg +2
While factual correctness and task-performance have been in focus of Large Language Model (LLM) research for a long time, the fundamental question of how human-like generated texts…
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?
Mitja Nikolaus, Emmanuelle Salin, Stephane Ayache +2
Recent advances in vision-and-language modeling have seen the development of Transformer architectures that achieve remarkable performance on multimodal reasoning tasks. Yet, the e…
Masked ELMo: An evolution of ELMo towards fully contextual RNN language models
Gregory Senay, Emmanuelle Salin
This paper presents Masked ELMo, a new RNN-based model for language model pre-training, evolved from the ELMo language model. Contrary to ELMo which only uses independent left-to-r…